Time series data is recorded for a number of business processes in order to identify past performance metrics and to predict future performance. Such time series data has been used to model various machines, such as printing devices, to determine the rate at which such machines use consumables. However, inaccurate forecasting of time series data reduces operational efficiency and can cause unnecessary expense due to excess or shortage of inventory, non-optimal resource usage, premium freight or labor charges, or the like. One challenge is finding a modeling framework that most accurately describes a wide variety of observed behavior among customers or machines and for different time periods for a particular customer or machine.